Calibration method based on blasting vibration acquisition instrument

Abstract

The invention discloses a calibration method based on a blasting vibration acquisition instrument, which comprises the following steps of: data acquisition: performing blasting tests at different delay times, recording vibration waveform data by using the acquisition instrument, synchronously counting bulk distribution after blasting, and establishing an associated database of the delay times and crushing effects; calibrating by an improved linear superposition method: decomposing an actually measured single-hole waveform by using Fourier series, adding a random variable to generate a simulation waveform, calculating a theoretical particle peak vibration velocity mean value at different delay times through Monte Carlo simulation, and comparing the actually measured particle peak vibration velocity of the acquisition instrument with a theoretical value; verifying the rock crushing effect: carrying out regression analysis on the calibrated vibration data and the rock crushing effect; testing stability and repeatability: repeatedly blasting under the same delay time, checking repeatability errors of data of the acquisition instrument, and analyzing the influence of background noise on the acquired data; and calibration result output: establishing a delay time-vibration parameter-crushing effect calibration curve.

Description

Technical Field

[0001] The present invention relates to the technical field of blasting engineering monitoring, and in particular to a calibration method based on a blasting vibration collector. Background Art

[0002] During blasting operations, the shattering of rock by explosives is accompanied by vibrations on the surrounding ground surface. Blasting vibrations can damage or destroy buildings within a certain distance, leading to extensive research on blasting vibrations by scholars both domestically and internationally. The vibrations generated by blasting operations can damage surrounding structures, necessitating safety through control of vibration intensity (e.g., peak particle velocity (PPV)). The delay time (the time interval between blasthole detonations) directly impacts the vibration superposition effect and rock fragmentation, but its optimal selection requires a balance between vibration attenuation and crushing quality. Blasting vibration collectors are key devices used to monitor and analyze blasting vibration effects in blasting operations. They are designed to capture the vibration energy released during rock fragmentation following explosive detonation.

[0003] In the existing technology, blasting vibration parameters and rock crushing quality are often analyzed independently, lacking a quantitative correlation basis, resulting in parameter optimization relying on experience. At the same time, conventional calibration methods rely on single waveform comparison and do not consider signal randomness and statistical laws, resulting in large deviations in calibration results. Therefore, a calibration method based on a blasting vibration collector is proposed. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a calibration method based on a blasting vibration collector.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions: A calibration method based on a blasting vibration collector comprises the following steps: Data acquisition: Blasting tests were conducted at different delay times. Vibration waveform data (including particle peak velocity (PPV), main frequency, duration, etc.) were recorded using a data acquisition instrument. The distribution of large pieces after blasting was simultaneously counted. The distribution of large pieces indicates the crushing effect. A database correlating delay time and crushing effect was established. Improved linear superposition calibration: Utilize Fourier series decomposition of the measured single-hole waveform, add random variables to generate a simulated waveform, and calculate the mean theoretical PPV at different delay times through Monte Carlo simulation. Compare the PPV measured by the collector with the theoretical value. If the deviation exceeds a threshold (±5%), adjust the collector's frequency response correction factor or amplitude gain parameter to align the two trends. Verification of rock crushing effect: Perform regression analysis on the calibrated vibration data and the rock crushing effect (large block quantity) to verify whether the correlation between vibration parameters (such as PPV and main frequency) and crushing quality is consistent with the reported conclusions (for example, the large block quantity is the smallest when the delay time is 40ms). If the crushing effect does not match the vibration parameters, further adjust the time integration algorithm of the data collector; Stability and repeatability testing: Repeat blasting at the same delay time to verify the repeatability error of the data acquisition instrument (standard deviation required to be <3%), analyze the impact of background noise (such as wind noise and mechanical vibration) on the collected data, and optimize signal quality through wavelet denoising or adaptive filtering algorithms; Calibration result output: Establish a calibration curve of delay time-vibration parameters-crushing effect, and record the calibration parameters (such as gain coefficient, filter setting), error range and applicable conditions.

[0006] The above further includes: Furthermore, the use of a data collector to record vibration waveform data (including particle peak velocity (PPV), main frequency, duration, etc.), synchronously calculating the distribution of large pieces after blasting, and establishing a database correlating delay time with crushing effect includes the following steps: Experimental design and data collection: Blasting was performed at different delay times, and the vibration waveform and crushing effect were recorded simultaneously. The data acquisition instrument recorded the vibration waveform (PPV, main frequency, duration), and the large block rate was manually calculated; Vibration waveform analysis and feature extraction: The peak velocity (PPV) and duration are calculated by formula, and the time domain waveform is converted into numerical features. The main frequency reflects the energy concentration frequency, and the duration reflects the vibration attenuation speed. The peak velocity (PPV) calculation formula is , the main frequency is analyzed by fast Fourier transform (FFT) waveform, and the maximum energy frequency is taken. The duration represents the total time that the vibration speed exceeds the threshold (0.1m / s); Establish a correlation model between delay time and crushing effect: Through correlation analysis, the causal relationship between delay time and vibration / crushing is confirmed, and the Pearson correlation coefficient between delay time and crushing effect parameters is calculated. The calculation formula is expressed as follows: ,in, and They are respectively represented as the actual value and predicted value of the delay time, and The actual value and predicted value of the bulk amount are expressed respectively. A regression model of the bulk amount and vibration parameters is established, and the weights in the coefficient regression model are fitted by the least squares method.

[0007] Furthermore, the method of using Fourier series to decompose the measured single-hole waveform, adding random variables to generate a simulated waveform, and calculating the mean value of the theoretical particle peak velocity (PPV) at different delay times through Monte Carlo simulation includes the following steps: Measured single-hole waveform acquisition and Fourier series decomposition: Using a blasting vibration collector to record the vibration waveform of a single-hole blasting , the sampling frequency is , the time length is , decompose the measured waveform into Fourier series and express it as ,in, is the fundamental angular frequency, , ; Add random variables to generate analog waveforms: Add random phases to each frequency component of Fourier decomposition , the generated simulation waveform is represented as ,in, ; Synthesized waveform: Define delay time , the synthesized waveform is expressed as ; Calculate the mean value of the peak particle velocity (PPV): Calculate the synthetic waveform Peak vibration velocity, repeatedly adding random variables to generate simulated waveforms, synthetic waveforms and calculated synthetic waveforms The peak vibration velocity is measured M times, and the mean value of the particle peak vibration velocity (PPV) is calculated, which is the theoretical particle peak vibration velocity (PPV) mean value.

[0008] Furthermore, the specific steps of comparing the measured peak particle velocity (PPV) of the collector with the theoretical value and adjusting the frequency response correction coefficient or amplitude gain parameter of the collector are as follows: Theoretical value: Get the theoretical mean value of the peak particle velocity (PPV); Obtaining measured values: Record the blasting vibration signal through a data acquisition instrument and extract the peak vibration velocity of the particle; Deviation calculation: Calculate the relative deviation between the measured value and the theoretical value. The calculation formula is expressed as ,in, is the mean value of the theoretical peak particle velocity (PPV), is the measured value. If the deviation exceeds , adjust the parameters; Parameter adjustment: If the measured value is too high, increase the frequency response correction coefficient (FRC); otherwise, decrease it; if the measured value is too low, increase the amplitude gain parameter (G) to compensate for signal attenuation; Verification after adjustment: Repeat the measured value acquisition, deviation calculation and parameter adjustment until .

[0009] Furthermore, the calibrated vibration data is subjected to regression analysis with the rock crushing effect (large block amount). If the crushing effect does not match the vibration parameters, the specific steps of further adjusting the time integration algorithm or energy calculation model of the collector are as follows: Regression analysis to verify the correlation: verify whether the correlation between vibration parameters (PPV, main frequency) and crushing quality (large block quantity) is consistent with the reported conclusions. Calculate the correlation coefficient and regression coefficient significance of the regression model between large block quantity and vibration parameters based on the real-time collected data. Check the vibration parameter calculation model: If the correlation is inconsistent, check the vibration parameter calculation model; Adjust the time integration algorithm: perform high-pass filtering on the original acceleration signal and re-integrate to calculate the peak particle velocity (PPV); Optimize energy calculation model: introduce frequency weighted energy model, expressed as E, where is the power spectrum density (reflecting energy distribution), For frequency weighting functions (e.g., enhancing high-frequency contributions), recalculate the energy of each frequency band and perform regression analysis with the bulk quantity; Regression analysis verification: using the corrected peak particle velocity (PPV) and frequency-weighted energy as independent variables and the proportion of large blocks as the dependent variable, regression analysis verification was performed again.

[0010] Furthermore, the checking vibration parameter calculation model comprises the following steps: Initial hypothesis and verification: Based on Sadovsky's formula, verify whether the peak particle velocity (PPV) decays with distance and whether the dominant frequency matches the rock mass properties. If the trend of the peak particle velocity (PPV) and dominant frequency does not match the expected trend (for example, the PPV increases while the amount of large blocks does not decrease), further check the calculation model; Signal processing algorithm review: Check whether the sensor sensitivity matches the data acquisition instrument settings, verify whether the sensor frequency response range covers the main frequency of blasting vibration, and check whether an inappropriate filter is used (low-pass filtering causes high-frequency components to be lost); Numerical simulation and comparison: Numerical software (LS-DYNA) was used to simulate the vibration of single-hole blasting and generate a theoretical acceleration signal. The same signal processing process as the measured value was applied to the theoretical signal to calculate the peak particle velocity (PPV) and main frequency, and the differences between the theoretical and measured values ​​were compared.

[0011] Furthermore, the specific steps of inspecting the repeatability error of the data collected by the acquisition instrument, analyzing the impact of background noise on the collected data, and optimizing the signal quality by wavelet denoising or adaptive filtering algorithm are as follows: Repeatability error test: Repeat the blasting at the same delay time to verify whether the repeatability error of the data collected by the data collector meets the standard deviation. requirements; Analysis of background noise impact: record background noise signals during the non-blasting period, record blasting signals and noisy signals, calculate the noise RMS value and signal RMS value, and calculate the signal-to-noise ratio based on the noise RMS value and signal RMS value; Wavelet Denoising: Selection Wavelet, decomposition level , perform multi-layer decomposition on the noisy signal to obtain approximate coefficients and detail coefficients, and use soft threshold ( is the noise standard deviation, N is the signal length), the detail coefficient (high-frequency noise) is threshold-contracted, and the denoised signal is reconstructed using the approximate coefficient and the processed detail coefficient: Adaptive filtering: Using the statistical characteristics of the background noise signal as a reference input, the filter weights are updated, the noise is estimated, and the updated background noise signal is output to obtain the noisy signal.

[0012] Furthermore, the specific steps of establishing a calibration curve of delay time-vibration parameter-crushing effect and recording calibration parameters (such as gain coefficient, filter setting), error range and applicable conditions are as follows: Data collection and processing: Collect vibration data of each blasting and calculate the maximum blasting vibration velocity at the measuring point; Analyze the crushing effect: observe and measure the rocks after blasting to evaluate the crushing effect.

[0013] Establish a calibration curve: draw a calibration curve with the delay time as the horizontal axis and the vibration parameters (such as the maximum blasting vibration velocity) and crushing effect indicators (such as the crushing fragment size) as the vertical axis.

[0014] The present invention has the following beneficial effects: In the present invention, by synchronously collecting vibration waveforms and the distribution of large pieces after blasting, a database related to delay time, vibration parameters and crushing effect is established, the causal relationship between vibration characteristics and crushing quality is clarified, and an improved linear superposition method is used, combined with Fourier series decomposition and Monte Carlo simulation, to generate a large number of simulated waveforms to calculate the theoretical PPV mean, so that the calibration benchmark is closer to the real physical process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a step diagram of a calibration method based on a blasting vibration collector proposed in the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] See also Figure 1 As shown, the present invention is a calibration method based on a blasting vibration collector, comprising the following steps: Data acquisition: Blasting tests were conducted at different delay times. Vibration waveform data (including particle peak velocity (PPV), main frequency, duration, etc.) were recorded using a data acquisition instrument. The distribution of large pieces after blasting was simultaneously counted. The distribution of large pieces indicates the crushing effect. A database correlating delay time and crushing effect was established. Improved linear superposition calibration: Utilize Fourier series decomposition of the measured single-hole waveform, add random variables to generate a simulated waveform, and calculate the mean theoretical PPV at different delay times through Monte Carlo simulation. Compare the PPV measured by the collector with the theoretical value. If the deviation exceeds a threshold (±5%), adjust the collector's frequency response correction factor or amplitude gain parameter to align the two trends. Verification of rock crushing effect: Perform regression analysis on the calibrated vibration data and the rock crushing effect (large block quantity) to verify whether the correlation between vibration parameters (such as PPV and main frequency) and crushing quality is consistent with the reported conclusions (for example, the large block quantity is the smallest when the delay time is 40ms). If the crushing effect does not match the vibration parameters, further adjust the time integration algorithm of the data collector; Stability and repeatability testing: Repeat blasting at the same delay time to verify the repeatability error of the data acquisition instrument (standard deviation required to be <3%), analyze the impact of background noise (such as wind noise and mechanical vibration) on the collected data, and optimize signal quality through wavelet denoising or adaptive filtering algorithms; Calibration result output: Establish a calibration curve of delay time-vibration parameters-crushing effect, and record the calibration parameters (such as gain coefficient, filter setting), error range and applicable conditions.

[0018] In one embodiment, the method of using a data collector to record vibration waveform data (including particle peak velocity (PPV), main frequency, duration, etc.), simultaneously calculating the distribution of large blocks after blasting, and establishing a database correlating delay time with crushing effect includes the following steps: Experimental design and data collection: Blasting was performed at different delay times, and the vibration waveform and crushing effect were recorded simultaneously. The data acquisition instrument recorded the vibration waveform (PPV, main frequency, duration), and the large block rate was manually calculated; Test conditions: Rock type: Granite (uniaxial compressive strength 80 MPa) Hole diameter: φ100mm, hole depth 12m, hole distance 3m Charge amount: Single hole charge 40kg, total charge 160kg (4 holes) Measuring point arrangement: A vibration collector (three-axis velocity sensor, sampling rate 2kHz) is arranged 20m away from the explosion source. Delay time setting: 4 groups of experiments: delay time is 0ms (simultaneous), 25ms, 50ms, and 75ms Data collection parameters: Vibration parameters: Particle Peak Velocity (PPV, m / s), Main Frequency (Hz), Duration (ms) Crushing effect parameters: large block ratio after blasting (the proportion of rock blocks with a volume greater than 0.5m³); Vibration waveform analysis and feature extraction: The peak velocity (PPV) and duration are calculated by formula, and the time domain waveform is converted into numerical features. The main frequency reflects the energy concentration frequency, and the duration reflects the vibration attenuation speed. The peak velocity (PPV) calculation formula is , the main frequency is analyzed by fast Fourier transform (FFT) waveform, and the maximum energy frequency is taken. The duration represents the total time that the vibration speed exceeds the threshold (0.1m / s); Measured data: Establish a correlation model between delay time and crushing effect: Through correlation analysis, the causal relationship between delay time and vibration / crushing is confirmed, and the Pearson correlation coefficient between delay time and crushing effect parameters is calculated. The calculation formula is expressed as follows: ,in, and They are respectively represented as the actual value and predicted value of the delay time, and The actual value and predicted value of the bulk amount are expressed respectively. A regression model of the bulk amount and vibration parameters is established, and the weights in the coefficient regression model are fitted by the least squares method.

[0019] In one embodiment, the method of using Fourier series to decompose the measured single-hole waveform, adding random variables to generate a simulated waveform, and calculating the mean of theoretical particle peak velocity (PPV) at different delay times through Monte Carlo simulation includes the following steps: Measured single-hole waveform acquisition and Fourier series decomposition: Using a blasting vibration collector to record the vibration waveform of a single-hole blasting , the sampling frequency is , the time length is , decompose the measured waveform into Fourier series and express it as ,in, is the fundamental angular frequency, , ; Add random variables to generate analog waveforms: Add random phases to each frequency component of Fourier decomposition , the generated simulation waveform is represented as ,in, ; Synthesized waveform: Define delay time , the synthesized waveform is expressed as ; Calculate the mean value of the peak particle velocity (PPV): Calculate the synthetic waveform Peak vibration velocity, repeatedly adding random variables to generate simulated waveforms, synthetic waveforms and calculated synthetic waveforms The peak vibration velocity is measured M times, and the mean value of the particle peak vibration velocity (PPV) is calculated, which is the theoretical particle peak vibration velocity (PPV) mean value.

[0020] In one embodiment, the specific steps of comparing the measured peak particle velocity (PPV) of the collector with the theoretical value and adjusting the frequency response correction coefficient or amplitude gain parameter of the collector are as follows: Theoretical value: Get the theoretical mean value of the peak particle velocity (PPV); Obtaining measured values: Record the blasting vibration signal through a data acquisition instrument and extract the peak vibration velocity of the particle; Deviation calculation: Calculate the relative deviation between the measured value and the theoretical value. The calculation formula is expressed as ,in, is the mean value of the theoretical peak particle velocity (PPV), is the measured value. If the deviation exceeds , adjust the parameters; Parameter adjustment: If the measured value is too high, increase the frequency response correction coefficient (FRC); otherwise, decrease it; if the measured value is too low, increase the amplitude gain parameter (G) to compensate for signal attenuation; Verification after adjustment: Repeat the measured value acquisition, deviation calculation and parameter adjustment until .

[0021] In one embodiment, the specific steps of performing regression analysis on the calibrated vibration data and the rock crushing effect (large block amount) and further adjusting the time integration algorithm or energy calculation model of the collector if the crushing effect does not match the vibration parameters are as follows: Regression analysis to verify the correlation: verify whether the correlation between vibration parameters (PPV, main frequency) and crushing quality (large block quantity) is consistent with the reported conclusions. Calculate the correlation coefficient and regression coefficient significance of the regression model between large block quantity and vibration parameters based on the real-time collected data. Measured data: The regression analysis showed that PPV was positively correlated with the crushing quality (p<0.05), and the main frequency was negatively correlated with the crushing quality (p<0.05), but the amount of large pieces was the smallest when the delay time was 40ms (consistent with the conclusion of the report).

[0022] If the report concludes that "large chunks are minimized at a delay of 40 ms," but regression analysis shows a positive correlation between PPV and crushing quality (i.e., the greater the PPV, the fewer large chunks), and the actual data shows that PPV is not maximized at 40 ms (16.8 cm / s < 18.1 cm / s), further verification is required; Check the vibration parameter calculation model: If the correlation is inconsistent, check the vibration parameter calculation model; Adjust the time integration algorithm: perform high-pass filtering on the original acceleration signal and re-integrate to calculate the peak particle velocity (PPV); Instance adjustment: After applying baseline correction to the raw data, recalculate the PPV: The PPV at a 40ms delay time dropped from 16.8cm / s to 15.5cm / s, but this is still not the maximum value. Further adjustments to the energy model are needed.

[0023] Optimize energy calculation model: introduce frequency weighted energy model, expressed as E, where is the power spectrum density (reflecting energy distribution), For frequency weighting functions (e.g., enhancing high-frequency contributions), recalculate the energy of each frequency band and perform regression analysis with the bulk quantity; Regression analysis verification: using the corrected peak particle velocity (PPV) and frequency-weighted energy as independent variables and the proportion of large blocks as the dependent variable, regression analysis verification was performed again.

[0024] In one embodiment, the checking of the vibration parameter calculation model comprises the following steps: Initial hypothesis and verification: Based on Sadovsky's formula, verify whether the peak particle velocity (PPV) decays with distance and whether the dominant frequency matches the rock mass properties. If the trend of the peak particle velocity (PPV) and dominant frequency does not match the expected trend (for example, the PPV increases while the amount of large blocks does not decrease), further check the calculation model; Signal processing algorithm review: Check whether the sensor sensitivity matches the data acquisition instrument settings, verify whether the sensor frequency response range covers the main frequency of blasting vibration, and check whether an inappropriate filter is used (low-pass filtering causes high-frequency components to be lost); Numerical simulation and comparison: Numerical software (LS-DYNA) was used to simulate the vibration of single-hole blasting and generate a theoretical acceleration signal. The same signal processing process as the measured value was applied to the theoretical signal to calculate the peak particle velocity (PPV) and main frequency, and the differences between the theoretical and measured values ​​were compared.

[0025] In one embodiment, the specific steps of inspecting the repeatability error of the data collected by the acquisition instrument, analyzing the impact of background noise on the collected data, and optimizing the signal quality by wavelet denoising or adaptive filtering algorithm are as follows: Repeatability error test: Repeat the blasting at the same delay time to verify whether the repeatability error of the data collected by the data collector meets the standard deviation. requirements; Analysis of background noise impact: record background noise signals during the non-blasting period, record blasting signals and noisy signals, calculate the noise RMS value and signal RMS value, and calculate the signal-to-noise ratio based on the noise RMS value and signal RMS value; Wavelet Denoising: Selection Wavelet, decomposition level , perform multi-layer decomposition on the noisy signal to obtain approximate coefficients and detail coefficients, and use soft threshold ( is the noise standard deviation, N is the signal length), the detail coefficient (high-frequency noise) is threshold-contracted, and the denoised signal is reconstructed using the approximate coefficient and the processed detail coefficient: Adaptive filtering: Using the statistical characteristics of the background noise signal as a reference input, the filter weights are updated, the noise is estimated, and the updated background noise signal is output to obtain the noisy signal.

[0026] In one embodiment, the specific steps of establishing a calibration curve of delay time-vibration parameter-crushing effect and recording calibration parameters (such as gain coefficient, filter setting), error range and applicable conditions are as follows: Data collection and processing: Collect vibration data of each blasting and calculate the maximum blasting vibration velocity at the measuring point; Analyze the crushing effect: observe and measure the rocks after blasting to evaluate the crushing effect.

[0027] Establish a calibration curve: draw a calibration curve with the delay time as the horizontal axis and the vibration parameters (such as the maximum blasting vibration velocity) and crushing effect indicators (such as the crushing fragment size) as the vertical axis.

[0028] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A calibration method based on a blasting vibration collector, characterized in that: The following steps are involved: Data acquisition: Blasting tests were conducted at different delay times. Vibration waveform data were recorded using a data acquisition instrument. The distribution of large pieces after blasting was simultaneously counted. The distribution of large pieces indicated the crushing effect. A database correlating delay time and crushing effect was established. Improved linear superposition calibration: Use Fourier series decomposition to decompose the measured single-hole waveform, add random variables to generate a simulated waveform, and use Monte Carlo simulation to calculate the mean of the theoretical particle peak velocity at different delay times. The measured particle peak velocity of the collector is compared with the theoretical value. If the deviation exceeds the threshold, adjust the frequency response correction factor or amplitude gain parameter of the collector to make the two trends consistent. Verification of rock crushing effect: Perform regression analysis on the calibrated vibration data and the rock crushing effect. If the crushing effect does not match the vibration parameters, further adjust the time integration algorithm of the collector. Stability and repeatability test: Repeat blasting at the same delay time to check the repeatability error of the data collected by the data collector, analyze the impact of background noise on the collected data, and optimize the signal quality through wavelet denoising or adaptive filtering algorithms; Calibration result output: Establish a calibration curve of delay time-vibration parameters-crushing effect, and record the calibration parameters, error range and applicable conditions.

2. The calibration method based on the blasting vibration collector according to claim 1, characterized in that: The method of using a collector to record vibration waveform data, synchronously counting the distribution of large pieces after blasting, and establishing a database correlating delay time with crushing effect includes the following steps: Experimental design and data collection: blasting at different delay times, synchronously recording vibration waveforms and crushing effects, the data acquisition instrument records the vibration waveforms, and manually calculates the large-piece rate; Vibration waveform analysis and feature extraction: The peak velocity and duration of the particle are calculated by formula, and the time domain waveform is converted into numerical features. The main frequency reflects the energy concentration frequency, and the duration reflects the vibration attenuation speed. The peak velocity calculation formula of the particle is The main frequency is analyzed by fast Fourier transform waveform to obtain the maximum energy frequency, and the duration represents the total time that the vibration speed exceeds the threshold value (0.1m / s); Establish a correlation model between delay time and crushing effect: Through correlation analysis, the causal relationship between delay time and vibration / crushing is confirmed, and the Pearson correlation coefficient between delay time and crushing effect parameters is calculated. The calculation formula is expressed as follows: ,in, and They are respectively represented as the actual value and predicted value of the delay time, and The actual value and predicted value of the bulk amount are expressed respectively. A regression model of the bulk amount and vibration parameters is established, and the weights in the coefficient regression model are fitted by the least squares method.

3. The calibration method based on the blasting vibration collector according to claim 1, characterized in that: The method of using Fourier series to decompose the measured single-hole waveform, adding random variables to generate a simulated waveform, and calculating the mean value of the theoretical particle peak vibration velocity under different delay times through Monte Carlo simulation includes the following steps: Measured single-hole waveform acquisition and Fourier series decomposition: Using a blasting vibration collector to record the vibration waveform of a single-hole blasting , the sampling frequency is , the time length is , decompose the measured waveform into Fourier series and express it as ,in, is the fundamental angular frequency, , ; Add random variables to generate analog waveforms: Add random phases to each frequency component of Fourier decomposition , the generated simulation waveform is represented as ,in, ; Synthesized waveform: Define delay time , the synthesized waveform is expressed as ; Calculate the mean of the peak velocity of the particle: Calculate the synthetic waveform Peak vibration velocity, repeatedly adding random variables to generate simulated waveforms, synthetic waveforms and calculated synthetic waveforms The peak vibration velocity of the particle is measured M times, and the average peak vibration velocity of the particle is calculated, which is the average peak vibration velocity of the theoretical particle.

4. The calibration method based on the blasting vibration collector according to claim 1, characterized in that: The specific steps of comparing the peak vibration velocity of the particle point measured by the acquisition instrument with the theoretical value and adjusting the frequency response correction coefficient or amplitude gain parameter of the acquisition instrument are as follows: Theoretical value: get the theoretical mean value of the peak vibration velocity of the particle; Obtaining measured values: Record the blasting vibration signal through a data acquisition instrument and extract the peak vibration velocity of the particle; Deviation calculation: Calculate the relative deviation between the measured value and the theoretical value. The calculation formula is expressed as ,in, is the mean value of the theoretical particle peak velocity, is the measured value. If the deviation exceeds , adjust the parameters; Parameter adjustment: If the measured value is too high, increase the frequency response correction factor; otherwise, decrease it; if the measured value is too low, increase the amplitude gain parameter to compensate for signal attenuation; Verification after adjustment: Repeat the measured value acquisition, deviation calculation and parameter adjustment until .

5. The calibration method based on the blasting vibration collector according to claim 2, characterized in that: The specific steps of performing regression analysis on the calibrated vibration data and the rock crushing effect and further adjusting the time integration algorithm or energy calculation model of the collector if the crushing effect does not match the vibration parameters are as follows: Regression analysis to verify the correlation: verify whether the correlation between vibration parameters and crushing quality is consistent with the report conclusion, and calculate the correlation coefficient and regression coefficient significance of the regression model between large block quantity and vibration parameters based on the real-time collected data; Check the vibration parameter calculation model: If the correlation is inconsistent, check the vibration parameter calculation model; When adjusting; Optimize energy calculation model: introduce frequency weighted energy model, expressed as E, where is the power spectral density, For the frequency weighting function, the energy of each frequency band is recalculated and regression analysis is performed with the bulk quantity; Regression analysis verification: Use the corrected particle peak velocity and frequency-weighted energy as independent variables, and the proportion of large blocks as the dependent variable, and re-regression analysis verification.

6. The calibration method based on the blasting vibration collector according to claim 5, characterized in that: The checking vibration parameter calculation model comprises the following steps: Initial hypothesis and verification: Based on Sadovsky's formula, verify whether the peak velocity of the particle decays with distance and whether the dominant frequency matches the rock mass properties. If the trend of the peak velocity and dominant frequency does not match the expectation, further check the calculation model; Signal processing algorithm review: Check whether the sensor sensitivity matches the data acquisition instrument settings, verify whether the sensor frequency response range covers the main frequency of blasting vibration, and check whether inappropriate filters are used; Numerical simulation and comparison: Use numerical software to simulate the vibration of single-hole blasting to generate a theoretical acceleration signal. Apply the same signal processing process as the measured value to the theoretical signal, calculate the peak vibration velocity and main frequency of the particle, and compare the differences between the theoretical and measured values.

7. The calibration method based on the blasting vibration collector according to claim 1, characterized in that: The specific steps of inspecting the repeatability error of the data collected by the acquisition instrument, analyzing the impact of background noise on the collected data, and optimizing the signal quality through wavelet denoising or adaptive filtering algorithm are as follows: Repeatability error test: Repeat the blasting at the same delay time to verify whether the repeatability error of the data collected by the data collector meets the standard deviation. requirements; Analysis of background noise impact: record background noise signals during the non-blasting period, record blasting signals and noisy signals, calculate the noise RMS value and signal RMS value, and calculate the signal-to-noise ratio based on the noise RMS value and signal RMS value; Wavelet Denoising: Selection Wavelet, decomposition level , perform multi-layer decomposition on the noisy signal to obtain approximate coefficients and detail coefficients, and use soft threshold , perform threshold shrinkage on the detail coefficients, and use the approximate coefficients and the processed detail coefficients to reconstruct the denoised signal: Adaptive filtering: Using the statistical characteristics of the background noise signal as a reference input, the filter weights are updated, the noise is estimated, and the updated background noise signal is output to obtain the noisy signal.

8. The calibration method based on the blasting vibration collector according to claim 1, characterized in that: The specific steps of establishing the calibration curve of delay time-vibration parameter-crushing effect and recording the calibration parameters, error range and applicable conditions are as follows: Data collection and processing: Collect vibration data of each blasting and calculate the maximum blasting vibration velocity at the measuring point; Analyze the crushing effect: observe and measure the rocks after blasting to evaluate the crushing effect; Establish a calibration curve: draw a calibration curve with the delay time as the horizontal axis and the vibration parameters and crushing effect indicators as the vertical axis.

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